Semantics & points of interest¶
Imports and configuration¶
The usual imports, click to see the details.
import numpy as np
import yaml
import torch
import plotly
import plotly.graph_objs as go
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode(connected=False)
import sys, os
# set software directory
software_dir = '%s/lartpc_mlreco3d' % os.environ.get('HOME')
sys.path.insert(0,software_dir)
from mlreco.visualization import scatter_points, plotly_layout3d
from mlreco.visualization.gnn import scatter_clusters, network_topology, network_schematic
from mlreco.utils.ppn import uresnet_ppn_type_point_selector
from mlreco.utils.cluster.dense_cluster import fit_predict, gaussian_kernel
from mlreco.main_funcs import process_config, prepare
from mlreco.utils.gnn.cluster import get_cluster_label
from mlreco.utils.deghosting import adapt_labels_numpy as adapt_labels
from mlreco.visualization.gnn import network_topology
from larcv import larcv
/usr/local/lib/python3.6/dist-packages/MinkowskiEngine/__init__.py:42: UserWarning:
The environment variable `OMP_NUM_THREADS` not set. MinkowskiEngine will automatically set `OMP_NUM_THREADS=16`. If you want to set `OMP_NUM_THREADS` manually, please export it on the command line before running a python script. e.g. `export OMP_NUM_THREADS=12; python your_program.py`. It is recommended to set it below 24.
Welcome to JupyROOT 6.22/02
And the configuration which is loaded from the file inference.cfg:
cfg=yaml.load(open('./inference.cfg', 'r'),Loader=yaml.Loader)
# pre-process configuration (checks + certain non-specified default settings)
process_config(cfg)
# prepare function configures necessary "handlers"
hs=prepare(cfg)
Config processed at: Linux volt005 3.10.0-1160.21.1.el7.x86_64 #1 SMP Tue Mar 16 18:28:22 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
$CUDA_VISIBLE_DEVICES="0"
{ 'iotool': { 'batch_size': 4,
'collate_fn': 'CollateSparse',
'dataset': { 'data_keys': [ './wire_mpvmpr_2020_04_test_small.root'],
'limit_num_files': 10,
'name': 'LArCVDataset',
'schema': { 'cluster_label': [ 'parse_cluster3d_clean_full',
'cluster3d_pcluster',
'particle_pcluster',
'particle_mpv',
'sparse3d_pcluster_semantics'],
'input_data': [ 'parse_sparse3d_scn',
'sparse3d_reco',
'sparse3d_reco_chi2'],
'kinematics_label': [ 'parse_cluster3d_kinematics_clean',
'cluster3d_pcluster',
'particle_corrected',
'particle_mpv',
'sparse3d_pcluster_semantics'],
'particle_graph': [ 'parse_particle_graph_corrected',
'particle_corrected',
'cluster3d_pcluster'],
'particles_asis': [ 'parse_particle_asis',
'particle_pcluster',
'cluster3d_pcluster'],
'particles_label': [ 'parse_particle_points_with_tagging',
'sparse3d_pcluster',
'particle_corrected'],
'segment_label': [ 'parse_sparse3d_scn',
'sparse3d_pcluster_semantics_ghost']}},
'minibatch_size': 4,
'num_workers': 4,
'shuffle': False},
'model': { 'loss_input': [ 'segment_label',
'particles_label',
'cluster_label',
'kinematics_label',
'particle_graph'],
'modules': { 'chain': { 'enable_cnn_clust': True,
'enable_cosmic': True,
'enable_ghost': True,
'enable_gnn_inter': True,
'enable_gnn_kinematics': True,
'enable_gnn_particle': False,
'enable_gnn_shower': True,
'enable_gnn_track': True,
'enable_ppn': True,
'enable_uresnet': True,
'use_ppn_in_gnn': True,
'verbose': True},
'cosmic_discriminator': { 'network_base': { 'data_dim': 3,
'features': 4,
'leakiness': 0.33,
'spatial_size': 768},
'res_encoder': { 'coordConv': True,
'latent_size': 2,
'pool_mode': 'avg'},
'uresnet_encoder': { 'features': 16,
'filters': 16,
'num_strides': 9},
'use_input_data': False,
'use_true_interactions': False},
'cosmic_loss': { 'node_loss': { 'balance_classes': True,
'name': 'type',
'target_col': 8}},
'dbscan_frag': { 'cluster_classes': [0, 2, 3],
'delta_label': 3,
'dim': 3,
'eps': [ 1.999,
3.999,
1.999,
4.999],
'michel_label': 2,
'min_samples': 1,
'min_size': [3, 3, 3, 3],
'num_classes': 4,
'ppn_distance_threshold': 1.999,
'ppn_mask_radius': 5,
'ppn_score_threshold': 0.9,
'ppn_type_threshold': 0.3,
'track_clustering_method': 'closest_path',
'track_label': 1},
'full_chain_loss': { 'clustering_weight': 1.0,
'cosmic_weight': 1.0,
'flow_weight': 1.0,
'inter_gnn_weight': 1.0,
'kinematics_p_weight': 1.0,
'kinematics_type_weight': 1.0,
'kinematics_weight': 1.0,
'particle_gnn_weight': 1.0,
'ppn_weight': 1.0,
'segmentation_weight': 1.0,
'shower_gnn_weight': 1.0,
'track_gnn_weight': 1.0},
'grappa_inter': { 'base': { 'add_start_dir': True,
'add_start_point': True,
'group_pred': 'score',
'kinematics_mlp': True,
'kinematics_momentum': False,
'kinematics_type': True,
'node_min_size': 3,
'node_type': [ 0,
1,
2,
3],
'start_dir_max_dist': 5,
'vertex_mlp': True},
'edge_encoder': { 'name': 'geo',
'use_numpy': False},
'gnn_model': { 'aggr': 'add',
'edge_classes': 2,
'edge_feats': 19,
'edge_output_feats': 64,
'leakiness': 0.1,
'name': 'meta',
'node_classes': 2,
'node_feats': 28,
'node_output_feats': 64,
'num_mp': 3},
'node_encoder': { 'name': 'geo',
'use_numpy': False},
'type_net': { 'num_hidden': 32},
'vertex_net': { 'num_hidden': 32}},
'grappa_inter_loss': { 'edge_loss': { 'balance_classes': False,
'high_purity': False,
'loss': 'CE',
'name': 'channel',
'reduction': 'sum',
'source_col': 6,
'target': 'group',
'target_col': 7},
'node_loss': { 'balance_classes': True,
'name': 'kinematics',
'spatial_size': 768,
'type_loss': 'CE'}},
'grappa_kinematics': { 'base': { 'edge_dist_metric': 'set',
'edge_dist_numpy': True,
'edge_max_dist': -1,
'kinematics_mlp': True,
'kinematics_momentum': True,
'kinematics_type': False,
'network': 'complete',
'node_min_size': -1,
'node_type': -1},
'edge_encoder': { 'cnn_encoder': { 'name': 'cnn2',
'network_base': { 'data_dim': 3,
'features': 4,
'leakiness': 0.33,
'spatial_size': 768},
'res_encoder': { 'coordConv': True,
'latent_size': 32,
'pool_mode': 'avg'},
'uresnet_encoder': { 'filters': 32,
'input_kernel': 3,
'num_classes': 5,
'num_filters': 32,
'num_strides': 9,
'reps': 2}},
'geo_encoder': { 'more_feats': True},
'name': 'mix_debug',
'normalize': True},
'gnn_model': { 'aggr': 'add',
'edge_classes': 2,
'edge_feats': 51,
'edge_output_feats': 64,
'leak': 0.33,
'name': 'nnconv_old',
'node_classes': 5,
'node_feats': 83,
'node_output_feats': 128,
'num_mp': 3},
'momentum_net': { 'num_hidden': 32},
'node_encoder': { 'cnn_encoder': { 'name': 'cnn2',
'network_base': { 'data_dim': 3,
'features': 4,
'leakiness': 0.33,
'spatial_size': 768},
'res_encoder': { 'coordConv': True,
'latent_size': 64,
'pool_mode': 'avg'},
'uresnet_encoder': { 'filters': 32,
'input_kernel': 3,
'num_classes': 5,
'num_filters': 16,
'num_strides': 9,
'reps': 2}},
'geo_encoder': { 'more_feats': True},
'name': 'mix_debug',
'normalize': True},
'use_true_particles': False},
'grappa_kinematics_loss': { 'edge_loss': { 'balance_classes': False,
'high_purity': False,
'name': 'channel',
'reduction': 'sum',
'target': 'particle_forest'},
'node_loss': { 'name': 'kinematics',
'reg_loss': 'l2'}},
'grappa_particle': { 'base': { 'node_min_size': 10,
'node_type': -1},
'edge_encoder': { 'name': 'geo',
'use_numpy': True},
'gnn_model': { 'aggr': 'add',
'edge_classes': 2,
'edge_feats': 19,
'edge_output_feats': 64,
'leakiness': 0.1,
'name': 'meta',
'node_classes': 2,
'node_feats': 24,
'node_output_feats': 64,
'num_mp': 3},
'node_encoder': { 'name': 'geo',
'use_numpy': True}},
'grappa_particle_loss': { 'edge_loss': { 'balance_classes': False,
'high_purity': True,
'loss': 'CE',
'name': 'channel',
'reduction': 'sum',
'source_col': 5,
'target': 'group',
'target_col': 6},
'node_loss': { 'balance_classes': False,
'group_pred_alg': 'score',
'high_purity': True,
'loss': 'CE',
'name': 'primary',
'reduction': 'sum',
'use_group_pred': True}},
'grappa_shower': { 'base': { 'add_start_dir': True,
'add_start_point': True,
'node_min_size': 3,
'node_type': 0,
'start_dir_max_dist': 5},
'edge_encoder': { 'name': 'geo',
'use_numpy': False},
'gnn_model': { 'aggr': 'add',
'edge_classes': 2,
'edge_feats': 19,
'edge_output_feats': 64,
'leakiness': 0.1,
'name': 'meta',
'node_classes': 2,
'node_feats': 28,
'node_output_feats': 64,
'num_mp': 3},
'node_encoder': { 'name': 'geo',
'use_numpy': False}},
'grappa_shower_loss': { 'edge_loss': { 'high_purity': True,
'name': 'channel',
'source_col': 5,
'target_col': 6},
'node_loss': { 'group_pred_alg': 'score',
'high_purity': True,
'name': 'primary',
'use_group_pred': True}},
'grappa_track': { 'base': { 'add_start_dir': True,
'add_start_point': True,
'node_min_size': 3,
'node_type': 1,
'start_dir_max_dist': 5},
'edge_encoder': { 'name': 'geo',
'use_numpy': False},
'gnn_model': { 'aggr': 'add',
'edge_classes': 2,
'edge_feats': 19,
'edge_output_feats': 64,
'leakiness': 0.1,
'name': 'meta',
'node_classes': 2,
'node_feats': 28,
'node_output_feats': 64,
'num_mp': 3},
'node_encoder': { 'name': 'geo',
'use_numpy': False}},
'grappa_track_loss': { 'edge_loss': { 'high_purity': False,
'name': 'channel',
'source_col': 5,
'target_col': 6}},
'spice': { 'fragment_clustering': { 'cluster_all': False,
'cluster_classes': [ 1],
'min_frag_size': 10,
'min_voxels': 2,
'p_thresholds': [ 0.95,
0.95,
0.95,
0.95],
's_thresholds': [ 0.0,
0.0,
0.0,
0.35]},
'network_base': { 'data_dim': 3,
'features': 4,
'leakiness': 0.33,
'spatial_size': 768},
'spatial_embeddings': { 'coordConv': True,
'embedding_dim': 3,
'seediness_dim': 1,
'sigma_dim': 1},
'uresnet': { 'filters': 64,
'input_kernel_size': 7,
'num_strides': 7,
'reps': 2}},
'spice_loss': { 'embedding_weight': 1.0,
'mask_loss_fn': 'lovasz_hinge',
'min_voxels': 2,
'name': 'se_vectorized_inter',
'seediness_weight': 1.0,
'smoothing_weight': 1.0},
'uresnet_ppn': { 'ppn': { 'classify_endpoints': True,
'data_dim': 3,
'downsample_ghost': True,
'filters': 16,
'model_name': 'ppn',
'model_path': './weights_ppn3_snapshot-1999.ckpt',
'num_classes': 5,
'num_strides': 6,
'ppn1_size': 24,
'ppn2_size': 96,
'ppn_num_conv': 1,
'score_threshold': 0.5,
'spatial_size': 768,
'use_encoding': False,
'weight_ppn': 0.9},
'uresnet_lonely': { 'data_dim': 3,
'features': 2,
'filters': 16,
'freeze': False,
'ghost': True,
'leakiness': 0.0,
'num_classes': 5,
'num_strides': 6,
'spatial_size': 768,
'weight_loss': True}}},
'name': 'full_chain',
'network_input': ['input_data']},
'trainval': { 'checkpoint_step': 100,
'concat_result': [ 'seediness',
'margins',
'embeddings',
'fragments',
'fragments_seg',
'shower_fragments',
'shower_edge_index',
'shower_edge_pred',
'shower_node_pred',
'shower_group_pred',
'track_fragments',
'track_edge_index',
'track_node_pred',
'track_edge_pred',
'track_group_pred',
'particle_fragments',
'particle_edge_index',
'particle_node_pred',
'particle_edge_pred',
'particle_group_pred',
'particles',
'inter_edge_index',
'inter_node_pred',
'inter_edge_pred',
'node_pred_p',
'node_pred_type',
'flow_edge_pred',
'kinematics_particles',
'kinematics_edge_index',
'clust_fragments',
'clust_frag_seg',
'interactions',
'inter_cosmic_pred',
'node_pred_vtx',
'total_num_points',
'total_nonghost_points'],
'debug': False,
'gpus': [0],
'iterations': 652,
'log_dir': './log_trash',
'minibatch_size': -1,
'model_path': './weights_full5_snapshot-999.cpkt',
'optimizer': {'args': {'lr': 0.001}, 'name': 'Adam'},
'report_step': 1,
'seed': 123,
'train': False,
'unwrapper': 'unwrap_3d_scn',
'weight_prefix': './weights_trash/snapshot'}}
Loading file: ./wire_mpvmpr_2020_04_test_small.root
Loading tree sparse3d_reco
Loading tree sparse3d_reco_chi2
Loading tree sparse3d_pcluster_semantics_ghost
Loading tree cluster3d_pcluster
Loading tree particle_pcluster
Loading tree particle_mpv
Loading tree sparse3d_pcluster_semantics
Loading tree sparse3d_pcluster
Loading tree particle_corrected
Sequential(
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(0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): NetworkInNetwork64->4
)
(1): OutputLayer()
)
ClusterCNN(
(input): Sequential(
(0): InputLayer()
(1): SubmanifoldConvolution 4->64 C7
)
(concat): JoinTable()
(add): AddTable()
(encoding_block): Sequential(
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(2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 64->64 C3
)
)
(1): AddTable()
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(2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 384->384 C3
)
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(3): AddTable()
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(3): SubmanifoldConvolution 448->448 C3
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(3): SubmanifoldConvolution 448->448 C3
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(encoding_conv): Sequential(
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(1): Sequential(
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(1): Convolution 128->192 C2/2
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)
(4): Sequential(
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(5): Sequential(
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(6): Sequential()
)
(decoding_block): Sequential(
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(3): SubmanifoldConvolution 384->384 C3
)
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(2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 384->384 C3
)
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(3): AddTable()
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(0): BatchNormLeakyReLU(640,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 640->320 C3
(2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 320->320 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 320->320 C3
(2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 320->320 C3
)
)
(3): AddTable()
)
(2): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork512->256
(1): Sequential(
(0): BatchNormLeakyReLU(512,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 512->256 C3
(2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 256->256 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 256->256 C3
(2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 256->256 C3
)
)
(3): AddTable()
)
(3): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork384->192
(1): Sequential(
(0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 384->192 C3
(2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 192->192 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 192->192 C3
(2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 192->192 C3
)
)
(3): AddTable()
)
(4): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork256->128
(1): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 256->128 C3
(2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 128->128 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 128->128 C3
(2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 128->128 C3
)
)
(3): AddTable()
)
(5): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork128->64
(1): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 128->64 C3
(2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 64->64 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 64->64 C3
(2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 64->64 C3
)
)
(3): AddTable()
)
)
(decoding_conv): Sequential(
(0): Sequential(
(0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 448->384 C2/2
)
(1): Sequential(
(0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 384->320 C2/2
)
(2): Sequential(
(0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 320->256 C2/2
)
(3): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 256->192 C2/2
)
(4): Sequential(
(0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 192->128 C2/2
)
(5): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 128->64 C2/2
)
)
(decoding_block2): Sequential(
(0): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork768->384
(1): Sequential(
(0): BatchNormLeakyReLU(768,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 768->384 C3
(2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 384->384 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 384->384 C3
(2): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 384->384 C3
)
)
(3): AddTable()
)
(1): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork640->320
(1): Sequential(
(0): BatchNormLeakyReLU(640,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 640->320 C3
(2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 320->320 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 320->320 C3
(2): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 320->320 C3
)
)
(3): AddTable()
)
(2): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork512->256
(1): Sequential(
(0): BatchNormLeakyReLU(512,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 512->256 C3
(2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 256->256 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 256->256 C3
(2): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 256->256 C3
)
)
(3): AddTable()
)
(3): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork384->192
(1): Sequential(
(0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 384->192 C3
(2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 192->192 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 192->192 C3
(2): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 192->192 C3
)
)
(3): AddTable()
)
(4): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork256->128
(1): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 256->128 C3
(2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 128->128 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 128->128 C3
(2): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 128->128 C3
)
)
(3): AddTable()
)
(5): Sequential(
(0): ConcatTable(
(0): NetworkInNetwork128->64
(1): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 128->64 C3
(2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 64->64 C3
)
)
(1): AddTable()
(2): ConcatTable(
(0): Identity()
(1): Sequential(
(0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): SubmanifoldConvolution 64->64 C3
(2): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(3): SubmanifoldConvolution 64->64 C3
)
)
(3): AddTable()
)
)
(decoding_conv2): Sequential(
(0): Sequential(
(0): BatchNormLeakyReLU(448,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 448->384 C2/2
)
(1): Sequential(
(0): BatchNormLeakyReLU(384,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 384->320 C2/2
)
(2): Sequential(
(0): BatchNormLeakyReLU(320,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 320->256 C2/2
)
(3): Sequential(
(0): BatchNormLeakyReLU(256,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 256->192 C2/2
)
(4): Sequential(
(0): BatchNormLeakyReLU(192,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 192->128 C2/2
)
(5): Sequential(
(0): BatchNormLeakyReLU(128,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): Deconvolution 128->64 C2/2
)
)
(outputEmbeddings): Sequential(
(0): Sequential(
(0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): NetworkInNetwork64->4
)
(1): OutputLayer()
)
(outputSeediness): Sequential(
(0): Sequential(
(0): BatchNormLeakyReLU(64,eps=0.0001,momentum=0.99,affine=True,leakiness=0.33)
(1): NetworkInNetwork64->1
)
(1): OutputLayer()
)
(tanh): Tanh()
(sigmoid): Sigmoid()
)
Total Number of Trainable Parameters = 175210432
Restoring weights for from ./weights_full5_snapshot-999.cpkt...
INCOMPATIBLE KEYS!
['module.uresnet_lonely.bn.weight', 'module.uresnet_lonely.bn.bias', 'module.uresnet_lonely.bn.running_mean', 'module.uresnet_lonely.bn.running_var']
make sure your module is named
Done.
The output is hidden because it reprints the entire (lengthy) configuration. Feel free to take a look if you are curious!
Finally we run the chain for 1 iteration:
# Call forward to run the net, store the output in "res"
data, output = hs.trainer.forward(hs.data_io_iter)
Segmentation Accuracy: 0.9882
PPN Accuracy: 0.5763
Clustering Accuracy: 0.9465
Shower fragment clustering accuracy: 1.0000
Shower primary prediction accuracy: 0.0000
Track fragment clustering accuracy: 0.9907
Particle ID accuracy: -1.2915
Interaction grouping accuracy: 0.9621
Flow accuracy: 0.9841
Type accuracy: 0.7500
Momentum accuracy: -3.7679
Vertex position accuracy: 0.6863
Vertex score accuracy: 0.9111
Cosmic discrimination accuracy: 0.8696
Now we can play with data and output to visualize what we are interested in.
Semantic segmentation (UResNet)¶
Let us take a look at the first entry.
entry = 0
We extract quantities of interest from the data and output dictionaries.
Here, we want the input_data (voxel coordinates and corresponding reconstructed energy depositions)
and segment_label (true semantic labels for each voxel). We will use the predicted ghost_mask
(binary mask ghost / non-ghost voxel) and the UResNet predictions segment_pred are obtained
from output['segmentation'] (softmax scores).
input_data = data['input_data'][entry]
segment_label = data['segment_label'][entry][:, -1]
ghost_mask = output['ghost'][entry].argmax(axis=1) == 0
segment_pred = output['segmentation'][entry].argmax(axis=1)
We use Plotly to visualize the result:
trace = []
edep = input_data[segment_label < 5]
trace+= scatter_points(input_data[segment_label < 5],markersize=1,color=segment_label[segment_label < 5], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3)
trace[-1].name = 'True semantic labels (true no-ghost mask)'
trace+= scatter_points(input_data[ghost_mask],markersize=1,color=segment_pred[ghost_mask], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3)
trace[-1].name = 'Predicted semantic labels (predicted no-ghost mask)'
fig = go.Figure(data=trace,layout=plotly_layout3d())
fig.update_layout(legend=dict(x=1.0, y=0.8))
iplot(fig)
Points of interest (PPN)¶
PPN makes a prediction for each non-zero voxel. We need to apply a post-processing function to apply the predicted attention mask and bring down the number of point proposals.
ppn = uresnet_ppn_type_point_selector(data['input_data'][entry], output, entry=entry,
score_threshold=0.5, type_threshold=2)
ppn_voxels = ppn[:, :3]
ppn_score = ppn[:, 5]
ppn_type = ppn[:, 12]
ppn_endpoints = np.argmax(ppn[:, 13:], axis=1)
The columns of ppn contain in this order:
point coordinates x, y, z
batch id
detection score (2 softmax values)
occupancy (how many points were merge to this single point during post processing)
softmax scores for 5 semantic types
type prediction (max softmax score among 5 semantic types)
You can also use the softmax scores for the 5 semantic types to make finer point type predictions - for example at a vertex, you can expect these scores to be high for two or more types.
We remove points that have a high score for being Delta rays starting points:
delta_label = 3
is_not_delta = ppn[:, 7 + delta_label] < 0.5
ppn_voxels = ppn_voxels[is_not_delta]
ppn_score = ppn_score[is_not_delta]
ppn_type = ppn_type[is_not_delta]
ppn_endpoints = ppn_endpoints[is_not_delta]
And at last! We can visualize both the true and predicted points:
trace = []
trace+= scatter_points(input_data[ghost_mask],markersize=1,color=segment_pred[ghost_mask], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3)
trace[-1].name = 'Predicted semantic labels (predicted no-ghost mask)'
trace += scatter_points(ppn_voxels, markersize=5, color=ppn_type, cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3, hovertext=ppn_score)
trace[-1].name = "PPN predictions (w/ type prediction)"
trace += scatter_points(ppn_voxels[ppn_type == 1], markersize=5, color=ppn_endpoints[ppn_type == 1], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3, hovertext=ppn_endpoints)
trace[-1].name = "PPN predictions (start/end)"
trace += scatter_points(data['particles_label'][entry], markersize=5, color=data['particles_label'][entry][:, 4], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3)
trace[-1].name = "True point labels"
trace += scatter_points(data['particles_label'][entry], markersize=5, color=data['particles_label'][entry][:, 6], cmin=0, cmax=10, colorscale=plotly.colors.qualitative.D3)
trace[-1].name = "True point labels (start/end)"
fig = go.Figure(data=trace,layout=plotly_layout3d())
fig.update_layout(legend=dict(x=1.0, y=0.8))
iplot(fig)
The color of the points corresponds to either their semantic type, or a binary start/end classification.
This is all there is to know about UResNet + PPN output and its visualization.